Welcome to Microsoft Azure AI Fundamentals AI-900 practice tests. This course will help you to test your preparation before attempting the certification exam.
The questions are as per the AI-900 exam syllabus of the official documentation.
We update these tests by adding new questions and additional practice tests - up to 6 practice tests (Max allowed by Udemy)
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Exam AI-900: Microsoft Azure AI Fundamentals:-
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Describe Artificial Intelligence workloads and considerations (15-20%)
Identify features of common AI workloads
identify prediction/forecasting workloads
identify features of anomaly detection workloads
identify computer vision workloads
identify natural language processing or knowledge mining workloads
identify conversational AI workloads
Identify guiding principles for responsible AI
describe considerations for fairness in an AI solution
describe considerations for reliability and safety in an AI solution
describe considerations for privacy and security in an AI solution
describe considerations for inclusiveness in an AI solution
describe considerations for transparency in an AI solution
describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (30- 35%)
Identify common machine learning types
identify regression machine learning scenarios
identify classification machine learning scenarios
identify clustering machine learning scenarios
Describe core machine learning concepts
identify features and labels in a dataset for machine learning
describe how training and validation datasets are used in machine learning
describe how machine learning algorithms are used for model training
select and interpret model evaluation metrics for classification and regression
Identify core tasks in creating a machine learning solution
describe common features of data ingestion and preparation
describe feature engineering and selection
describe common features of model training and evaluation
describe common features of model deployment and management
Describe capabilities of no-code machine learning with Azure Machine Learning studio
Describe features of computer vision workloads on Azure (15-20%)
Identify common types of computer vision solution:
identify features of image classification solutions
identify features of object detection solutions
identify features of semantic segmentation solutions
identify features of optical character recognition solutions
identify features of facial detection, facial recognition, and facial analysis solutions
Identify Azure tools and services for computer vision tasks
identify capabilities of the Computer Vision service
identify capabilities of the Custom Vision service
identify capabilities of the Face service
identify capabilities of the Form Recognizer service
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
Identify features of common NLP Workload Scenarios
identify features and uses for key-phrase extraction
identify features and uses for entity recognition
identify features and uses for sentiment analysis
identify features and uses for language modeling
identify features and uses for speech recognition and synthesis
identify features and uses for translation
Identify Azure tools and services for NLP workloads
identify capabilities of the Text Analytics service
identify capabilities of the Language Understanding service (LUIS)
identify capabilities of the Speech service
identify capabilities of the Translator Text service
Describe features of conversational AI workloads on Azure (15-20%)
Identify common use cases for conversational AI
identify features and uses for webchat bots
identify features and uses for telephone voice menus
identify features and uses for personal digital assistants
identify common characteristics of conversational AI solutions
Identify Azure services for conversational AI
